An integrated approach to dissect the functional network of large non-coding RNA

剖析大非编码 RNA 功能网络的综合方法

基本信息

批准号：
9001952
负责人：
Yiwen Chen
金额：
$ 20.2万
依托单位：
UNIVERSITY OF TX MD ANDERSON CAN CTR
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-02-01 至 2018-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9001952
关键词：
Algorithms Androgen Receptor Base Pairing Binding Bioinformatics Biometry Biophysics Categories Cell Line Clinical Communities Computational Biology Computing Methodologies DNA Sequence Alteration Dana-Farber Cancer Institute Data Data Analyses Data Set Development Enhancers Environment Exons Genomic approach Genomics Homologous Gene Human Cell Line Human Genome Immunoprecipitation K-Series Research Career Programs Machine Learning Malignant Neoplasms Malignant neoplasm of prostate Mediating Medicine Mentors Messenger RNA Methods MicroRNAs Modeling Molecular Biology Techniques Molecular Profiling PTEN gene Play Prostatic Neoplasms RNA RNA immunoprecipitation sequencing RNA-Protein Interaction Repression Research Research Personnel Resources Ribonucleoproteins Role Sampling Structure-Activity Relationship Technology Training Training Programs Tumor Suppression Untranslated RNA base career development chromatin immunoprecipitation experience mRNA Expression medical schools new therapeutic target next generation sequencing novel professor prostate carcinogenesis protein complex research and development research study skills statistics therapeutic target transcriptome sequencing tumor tumorigenesis

项目摘要

PROJECT SUMMARY Recent studies revealed that the human genome encodes thousands of lncRNAs with little proteincoding capacity. LncRNAs were shown to play important roles in cancer and are potentially a new class of therapeutic targets for cancer. However, the function of the vast majority of lncRNAs in cancer remains unknown. LncRNA function often depends on its physical interactions with protein complexes. They can also influence the abundance of other mRNAs that are targeted by the same microRNAs by competing for microRNA binding, i.e., serving as competing endogenous RNA (ceRNA). Advances in genomic technologies, especially those based on next generation sequencing (NGS), provide unparalleled opportunities to characterize the functional networks of lncRNA in cancer. However, analysis and integration of different types of genomic datasets to generate testable hypotheses is challenging, and systematic approaches to characterize lncRNA function in cancer are lacking. This application describes the development of computational methods and integrative genomic strategies for systematically dissecting the functional network of lncRNA in cancer, and a combination of computational and experimental approaches to unravel several important functional networks of lncRNA in prostate cancer. Specifically, it will (1) develop a computational method for repurposing the publically available array-based data to interrogate lncRNA expression in tumor samples and utilize an integrative genomic strategy to predict lncRNAs that may be important for tumorigenesis/tumor suppression in prostate cancer via analysis of lncRNA expression profiles, clinical information and somatic genomic alteration profiles of tumor samples, (2) identify the lncRNAs that are associated with EZH2 or direct transcriptional targets of EZH2 repression that are important for prostate tumorigenesis or tumor suppression, and (3) identify the ceRNAs of AR and PTEN that mediate prostate tumorigenesis or tumor suppression. In addition to its scientific proposal, this application proposes a comprehensive training program for preparing an independent investigator in the fields of computational genomics, noncoding RNA and cancer, who develops cutting-edge computational methods, and uses a combination of computational and experimental approaches to understand structure-function relationship of noncoding RNA and the function of noncoding RNA and RNA-protein interaction in cancer. While the candidate of this application has received extensive training in biophysics, statistics, machine learning and computational genomics, this career development award will allow him to develop his experimental skills, especially those next-generation sequencing-based techniques and molecular biology experiments in human cell lines. Dr. Liu, Professor of Biostatistics and Computational Biology and Dr. Brown, Professor of Medicine will mentor the candidate in the excellent training environment of Dana Farber Cancer Institute, a part of Harvard Medical School community. A committee of experienced computational and cancer biologists will also advise him on both scientific research and career development.

项目概要最近的研究表明，人类基因组编码数千个 lncRNA，但蛋白质编码很少容量。 LncRNA 已被证明在癌症中发挥重要作用，并有可能成为一类新型治疗药物癌症的目标。然而，绝大多数 lncRNA 在癌症中的功能仍不清楚。长链非编码RNA 功能通常取决于其与蛋白质复合物的物理相互作用。他们还可以影响通过竞争 microRNA 结合而被相同 microRNA 靶向的其他 mRNA 的丰度，即，充当竞争性内源 RNA (ceRNA)。基因组技术的进步，特别是基于下一代测序（NGS），提供了无与伦比的机会来表征功能癌症中的 lncRNA 网络。然而，不同类型的基因组数据集的分析和整合产生可检验的假设具有挑战性，并且系统性的方法来表征 lncRNA 功能缺乏癌症。该应用程序描述了计算方法和集成的发展系统剖析癌症中 lncRNA 功能网络的基因组策略，以及组合的计算和实验方法来解开 lncRNA 的几个重要功能网络前列腺癌。具体来说，它将 (1) 开发一种计算方法，用于重新利用公开可用的数据基于阵列的数据来询问肿瘤样本中的 lncRNA 表达并利用整合基因组预测可能对前列腺癌肿瘤发生/肿瘤抑制很重要的lncRNA的策略肿瘤lncRNA表达谱、临床信息和体细胞基因组改变谱分析样本，(2) 识别与 EZH2 相关的 lncRNA 或 EZH2 的直接转录靶标对前列腺肿瘤发生或肿瘤抑制很重要的抑制，以及（3）鉴定 AR 和 PTEN 介导前列腺肿瘤发生或肿瘤抑制。除了其科学建议外，该申请提出了一项全面的培训计划，旨在培养一名独立调查员计算基因组学、非编码RNA和癌症领域，谁开发了尖端技术计算方法，并结合计算和实验方法来理解结构功能非编码RNA与非编码RNA的功能以及RNA-蛋白质相互作用的关系癌症。虽然该应用程序的候选人接受过生物物理学、统计学、机器学等方面的广泛培训学习和计算基因组学，这个职业发展奖将使他能够发展他的实验技能，尤其是基于下一代测序的实验技能人类细胞系的技术和分子生物学实验。生物统计学和计算生物学教授刘博士和医学教授布朗博士将在丹娜法伯卓越的培训环境中指导候选人癌症研究所是哈佛医学院社区的一部分。由经验丰富的计算和癌症委员会组成生物学家还将在科学研究和职业发展方面为他提供建议。